Image tampering detection and localization model based on multi-scale edge information enhancement

Aiming at the problems of inaccurate localization of subtle tampering regions and fuzzy tampering boundaries in the field of digital image forensics, this paper proposes a lightweight adaptive image tampering detection model based on the fusion of multi-scale edge information enhancement and attention mechanism. Firstly, for the problem of blurred tamper boundary, a multi-scale edge information enhancement method is proposed for strengthening the edge features of the model, which improves the perception ability of the edge of the tamper region. Second, the improved lightweight adaptive feature extraction module reduces the number of model parameters, refines the feature extraction process, and effectively captures contextual information and detailed features. Finally, the multi-attention feature fusion strategy is used to improve the model’s ability to detect feature correlation and accurate localization. The experimental results show that the method achieves significant improvement on all three public datasets. On CASIA2.0 dataset, the F1 value reaches 0.895; on NIST16, the F1 value is 0.943; and the performance on Columbia is the best, and the overall performance outperforms that of the existing comparison methods.